Frenzy: Collaborative data organization for creating conference sessions
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Miller_frenzy.pdf
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Author(s)
Chilton, Lydia B.
Cordeiro, Felicia
Landay, James A.
Weld, Daniel S.
Dow, Steven P.
Miller, Robert C.
Zhang, Haoqi
Kim, Ju Ho
Andre, Paul
Date Issued
April 2014
Journal
Proceedings of the 32nd annual ACM conference on Human factors in computing systems (CHI '14)
Publisher
Association for Computing Machinery (ACM)
Citation
Lydia B. Chilton, Juho Kim, Paul Andre, Felicia Cordeiro, James A. Landay, Daniel S. Weld, Steven P. Dow, Robert C. Miller, and Haoqi Zhang. 2014. Frenzy: collaborative data organization for creating conference sessions. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems (CHI '14). ACM, New York, NY, USA, 1255-1264.
Version
Author's final manuscript
Abstract
Organizing conference sessions around themes improves the experience for attendees. However, the session creation process can be difficult and time-consuming due to the amount of expertise and effort required to consider alternative paper groupings. We present a collaborative web application called Frenzy to draw on the efforts and knowledge of an entire program committee. Frenzy comprises (a) interfaces to support large numbers of experts working collectively to create sessions, and (b) a two-stage process that decomposes the session-creation problem into meta-data elicitation and global constraint satisfaction. Meta-data elicitation involves a large group of experts working simultaneously, while global constraint satisfaction involves a smaller group that uses the meta-data to form sessions.
We evaluated Frenzy with 48 people during a deployment at the CSCW 2014 program committee meeting. The session making process was much faster than the traditional process, taking 88 minutes instead of a full day. We found that meta-data elicitation was useful for session creation. Moreover, the sessions created by Frenzy were the basis of the CSCW 2014 schedule.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2556288.2557375
